from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 2.299420 | 0.185296 | NaN | 0.000348 | 0.002299 | brute | -1 | 1 | 0.663 | 0.463811 | 0.007244 | 0.687 | 4.957669 | 4.958274 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 3.125257 | 0.066843 | NaN | 0.000256 | 0.003125 | brute | -1 | 5 | 0.757 | 0.467957 | 0.011975 | 0.742 | 6.678509 | 6.680695 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.444856 | 0.014145 | NaN | 0.000327 | 0.002445 | brute | 1 | 100 | 0.882 | 0.520501 | 0.007273 | 0.875 | 4.697122 | 4.697580 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.025754 | 0.001862 | NaN | 0.000031 | 0.025754 | brute | 1 | 100 | 1.000 | 0.010896 | 0.000573 | 0.000 | 2.363572 | 2.366836 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 3.145997 | 0.066497 | NaN | 0.000254 | 0.003146 | brute | -1 | 100 | 0.882 | 0.519073 | 0.008674 | 0.875 | 6.060799 | 6.061645 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.032688 | 0.003963 | NaN | 0.000024 | 0.032688 | brute | -1 | 100 | 1.000 | 0.013126 | 0.003514 | 0.000 | 2.490363 | 2.578088 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.457043 | 0.038789 | NaN | 0.000326 | 0.002457 | brute | 1 | 5 | 0.757 | 0.464816 | 0.005783 | 0.742 | 5.286054 | 5.286463 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.616419 | 0.026886 | NaN | 0.000495 | 0.001616 | brute | 1 | 1 | 0.663 | 0.462349 | 0.008985 | 0.687 | 3.496101 | 3.496761 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.822141 | 0.023534 | NaN | 0.000009 | 0.001822 | brute | -1 | 1 | 0.896 | 0.100050 | 0.003502 | 0.967 | 18.212233 | 18.223385 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.731999 | 0.022395 | NaN | 0.000006 | 0.002732 | brute | -1 | 5 | 0.922 | 0.102212 | 0.004119 | 0.974 | 26.728792 | 26.750488 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 2.164288 | 0.018295 | NaN | 0.000007 | 0.002164 | brute | 1 | 100 | 0.929 | 0.156137 | 0.005102 | 0.975 | 13.861484 | 13.868882 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.765121 | 0.046763 | NaN | 0.000006 | 0.002765 | brute | -1 | 100 | 0.929 | 0.156118 | 0.005172 | 0.975 | 17.711765 | 17.721482 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 2.152489 | 0.017996 | NaN | 0.000007 | 0.002152 | brute | 1 | 5 | 0.922 | 0.100269 | 0.001218 | 0.974 | 21.467215 | 21.468798 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.289950 | 0.012311 | NaN | 0.000012 | 0.001290 | brute | 1 | 1 | 0.896 | 0.100119 | 0.002262 | 0.967 | 12.884104 | 12.887391 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.674 | 0.0 | -1 | 1 | 0.060 | 0.006 | 0.234 | 0.235 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.768 | 0.0 | -1 | 5 | 0.057 | 0.001 | 0.243 | 0.243 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.912 | 0.0 | 1 | 100 | 0.058 | 0.001 | 0.233 | 0.233 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.843 | 0.0 | -1 | 100 | 0.062 | 0.007 | 0.219 | 0.221 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.015 | 0.001 | 5.332 | 0.0 | 1 | 5 | 0.057 | 0.001 | 0.264 | 0.264 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.014 | 0.001 | 5.761 | 0.0 | 1 | 1 | 0.058 | 0.001 | 0.241 | 0.241 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.344 | 0.0 | -1 | 1 | 0.009 | 0.001 | 0.533 | 0.534 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.310 | 0.0 | -1 | 5 | 0.009 | 0.000 | 0.572 | 0.573 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.332 | 0.0 | 1 | 100 | 0.009 | 0.000 | 0.515 | 0.515 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.001 | 0.300 | 0.0 | -1 | 100 | 0.008 | 0.001 | 0.631 | 0.634 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.322 | 0.0 | 1 | 5 | 0.008 | 0.001 | 0.610 | 0.612 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.334 | 0.0 | 1 | 1 | 0.009 | 0.000 | 0.560 | 0.561 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.299 | 0.185 | 0.0 | 0.002 | -1 | 1 | 0.464 | 0.007 | 4.958 | 4.958 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.029 | 0.003 | 0.0 | 0.029 | -1 | 1 | 0.011 | 0.001 | 2.536 | 2.542 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 3.125 | 0.067 | 0.0 | 0.003 | -1 | 5 | 0.468 | 0.012 | 6.679 | 6.681 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.030 | 0.004 | 0.0 | 0.030 | -1 | 5 | 0.013 | 0.002 | 2.436 | 2.455 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.445 | 0.014 | 0.0 | 0.002 | 1 | 100 | 0.521 | 0.007 | 4.697 | 4.698 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.026 | 0.002 | 0.0 | 0.026 | 1 | 100 | 0.011 | 0.001 | 2.364 | 2.367 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 3.146 | 0.066 | 0.0 | 0.003 | -1 | 100 | 0.519 | 0.009 | 6.061 | 6.062 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.033 | 0.004 | 0.0 | 0.033 | -1 | 100 | 0.013 | 0.004 | 2.490 | 2.578 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.457 | 0.039 | 0.0 | 0.002 | 1 | 5 | 0.465 | 0.006 | 5.286 | 5.286 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.025 | 0.001 | 0.0 | 0.025 | 1 | 5 | 0.012 | 0.001 | 2.148 | 2.158 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.616 | 0.027 | 0.0 | 0.002 | 1 | 1 | 0.462 | 0.009 | 3.496 | 3.497 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.001 | 0.0 | 0.024 | 1 | 1 | 0.011 | 0.001 | 2.164 | 2.170 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.822 | 0.024 | 0.0 | 0.002 | -1 | 1 | 0.100 | 0.004 | 18.212 | 18.223 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.004 | 0.0 | 0.006 | -1 | 1 | 0.001 | 0.000 | 9.253 | 9.331 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.732 | 0.022 | 0.0 | 0.003 | -1 | 5 | 0.102 | 0.004 | 26.729 | 26.750 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.010 | 0.003 | 0.0 | 0.010 | -1 | 5 | 0.001 | 0.000 | 16.304 | 16.337 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.164 | 0.018 | 0.0 | 0.002 | 1 | 100 | 0.156 | 0.005 | 13.861 | 13.869 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.004 | 0.000 | 0.0 | 0.004 | 1 | 100 | 0.001 | 0.000 | 4.825 | 4.835 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.765 | 0.047 | 0.0 | 0.003 | -1 | 100 | 0.156 | 0.005 | 17.712 | 17.721 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.0 | 0.006 | -1 | 100 | 0.001 | 0.000 | 8.735 | 8.779 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.152 | 0.018 | 0.0 | 0.002 | 1 | 5 | 0.100 | 0.001 | 21.467 | 21.469 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.0 | 0.003 | 1 | 5 | 0.001 | 0.000 | 5.215 | 5.235 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.290 | 0.012 | 0.0 | 0.001 | 1 | 1 | 0.100 | 0.002 | 12.884 | 12.887 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.0 | 0.002 | 1 | 1 | 0.001 | 0.000 | 3.743 | 3.750 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.877259 | 1.077265 | NaN | 0.000091 | 0.000877 | kd_tree | -1 | 1 | 0.929 | 0.130709 | 0.012519 | 0.910 | 6.711570 | 6.742282 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.134279 | 0.412869 | NaN | 0.000071 | 0.001134 | kd_tree | -1 | 5 | 0.946 | 0.227291 | 0.008137 | 0.941 | 4.990419 | 4.993616 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 6.121749 | 0.632406 | NaN | 0.000013 | 0.006122 | kd_tree | 1 | 100 | 0.951 | 0.714606 | 0.011837 | 0.940 | 8.566604 | 8.567780 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.530933 | 0.233538 | NaN | 0.000023 | 0.003531 | kd_tree | -1 | 100 | 0.951 | 0.688709 | 0.011866 | 0.940 | 5.126891 | 5.127651 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.854643 | 0.395971 | NaN | 0.000043 | 0.001855 | kd_tree | 1 | 5 | 0.946 | 0.235509 | 0.006124 | 0.941 | 7.875037 | 7.877699 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 1.013470 | 0.380790 | NaN | 0.000079 | 0.001013 | kd_tree | 1 | 1 | 0.929 | 0.124144 | 0.003136 | 0.910 | 8.163690 | 8.166295 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.033248 | 0.013085 | NaN | 0.000481 | 0.000033 | kd_tree | -1 | 1 | 0.891 | 0.000665 | 0.000093 | 0.879 | 49.968679 | 50.454293 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.030908 | 0.001397 | NaN | 0.000518 | 0.000031 | kd_tree | -1 | 5 | 0.911 | 0.000932 | 0.000058 | 0.905 | 33.150058 | 33.213179 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.046444 | 0.012362 | NaN | 0.000345 | 0.000046 | kd_tree | 1 | 100 | 0.894 | 0.005953 | 0.000253 | 0.917 | 7.801923 | 7.808967 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.049052 | 0.008449 | NaN | 0.000326 | 0.000049 | kd_tree | -1 | 100 | 0.894 | 0.005924 | 0.000181 | 0.917 | 8.279923 | 8.283799 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.028991 | 0.002245 | NaN | 0.000552 | 0.000029 | kd_tree | 1 | 5 | 0.911 | 0.000935 | 0.000095 | 0.905 | 31.008564 | 31.169005 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.026514 | 0.001769 | NaN | 0.000603 | 0.000027 | kd_tree | 1 | 1 | 0.891 | 0.000616 | 0.000036 | 0.879 | 43.070294 | 43.142123 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.898 | 0.065 | 0.028 | 0.0 | -1 | 1 | 0.885 | 0.137 | 3.274 | 3.313 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.877 | 0.053 | 0.021 | 0.0 | -1 | 5 | 0.827 | 0.009 | 4.690 | 4.690 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.987 | 0.078 | 0.020 | 0.0 | 1 | 100 | 0.801 | 0.014 | 4.978 | 4.979 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.967 | 0.107 | 0.020 | 0.0 | -1 | 100 | 0.832 | 0.027 | 4.771 | 4.773 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 4.004 | 0.054 | 0.020 | 0.0 | 1 | 5 | 0.791 | 0.007 | 5.061 | 5.061 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.958 | 0.068 | 0.020 | 0.0 | 1 | 1 | 0.830 | 0.010 | 4.770 | 4.771 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.019 | 0.0 | -1 | 1 | 0.004 | 0.003 | 0.216 | 0.258 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.020 | 0.0 | -1 | 5 | 0.002 | 0.002 | 0.332 | 0.400 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.022 | 0.0 | 1 | 100 | 0.002 | 0.001 | 0.480 | 0.567 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.023 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.672 | 0.673 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.024 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.638 | 0.638 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.024 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.629 | 0.631 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.877 | 1.077 | 0.000 | 0.001 | -1 | 1 | 0.131 | 0.013 | 6.712 | 6.742 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 10.424 | 10.683 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.134 | 0.413 | 0.000 | 0.001 | -1 | 5 | 0.227 | 0.008 | 4.990 | 4.994 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 0.000 | 0.000 | 8.123 | 8.417 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 6.122 | 0.632 | 0.000 | 0.006 | 1 | 100 | 0.715 | 0.012 | 8.567 | 8.568 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.413 | 3.504 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.531 | 0.234 | 0.000 | 0.004 | -1 | 100 | 0.689 | 0.012 | 5.127 | 5.128 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 5.841 | 5.912 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.855 | 0.396 | 0.000 | 0.002 | 1 | 5 | 0.236 | 0.006 | 7.875 | 7.878 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 0.001 | 0.000 | 3.284 | 3.558 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.013 | 0.381 | 0.000 | 0.001 | 1 | 1 | 0.124 | 0.003 | 8.164 | 8.166 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.556 | 3.622 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.033 | 0.013 | 0.000 | 0.000 | -1 | 1 | 0.001 | 0.000 | 49.969 | 50.454 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 23.486 | 23.901 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.031 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.150 | 33.213 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 14.778 | 15.658 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.046 | 0.012 | 0.000 | 0.000 | 1 | 100 | 0.006 | 0.000 | 7.802 | 7.809 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 4.933 | 5.042 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.049 | 0.008 | 0.000 | 0.000 | -1 | 100 | 0.006 | 0.000 | 8.280 | 8.284 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 0.000 | 0.000 | 16.548 | 16.788 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.029 | 0.002 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 31.009 | 31.169 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 4.860 | 4.972 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.027 | 0.002 | 0.001 | 0.000 | 1 | 1 | 0.001 | 0.000 | 43.070 | 43.142 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 5.691 | 5.724 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.625 | 0.084 | 30 | 0.026 | 0.0 | random | 0.325 | 0.010 | 1.924 | 1.925 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.694 | 0.029 | 30 | 0.023 | 0.0 | k-means++ | 0.377 | 0.022 | 1.843 | 1.846 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 7.769 | 0.342 | 30 | 0.103 | 0.0 | random | 4.197 | 0.026 | 1.851 | 1.851 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 8.101 | 0.027 | 30 | 0.099 | 0.0 | k-means++ | 4.449 | 0.075 | 1.821 | 1.821 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.000 | 30 | 0.008 | 0.000 | random | 0.0 | 0.0 | 7.490 | 8.841 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 11.882 | 11.929 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.007 | 0.000 | k-means++ | 0.0 | 0.0 | 9.164 | 9.506 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 10.721 | 10.924 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.001 | 30 | 0.342 | 0.000 | random | 0.0 | 0.0 | 7.300 | 7.559 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 11.428 | 11.527 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.356 | 0.000 | k-means++ | 0.0 | 0.0 | 7.235 | 7.434 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | k-means++ | 0.0 | 0.0 | 9.850 | 10.793 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.002342 | 0.000185 | 20 | 0.006831 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000705 | 0.000069 | -0.000965 | 3.320606 | 3.336294 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.002426 | 0.000185 | 20 | 0.006595 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000721 | 0.000085 | -0.000750 | 3.365719 | 3.388827 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.003676 | 0.000192 | 20 | 0.217626 | 0.000004 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.001609 | 0.000101 | 0.293767 | 2.284793 | 2.289254 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.003856 | 0.000724 | 20 | 0.207469 | 0.000004 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001610 | 0.000080 | 0.256968 | 2.394554 | 2.397495 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.113 | 0.007 | 20 | 0.001 | 0.0 | random | 0.058 | 0.002 | 1.950 | 1.952 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.326 | 0.004 | 20 | 0.000 | 0.0 | k-means++ | 0.147 | 0.004 | 2.224 | 2.225 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.357 | 0.006 | 20 | 0.022 | 0.0 | random | 0.283 | 0.005 | 1.264 | 1.264 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 1.200 | 0.027 | 20 | 0.007 | 0.0 | k-means++ | 0.649 | 0.004 | 1.849 | 1.849 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.000 | 20 | 0.007 | 0.000 | random | 0.001 | 0.0 | 3.321 | 3.336 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | random | 0.000 | 0.0 | 10.852 | 11.169 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.000 | 20 | 0.007 | 0.000 | k-means++ | 0.001 | 0.0 | 3.366 | 3.389 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 10.569 | 10.668 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.004 | 0.000 | 20 | 0.218 | 0.000 | random | 0.002 | 0.0 | 2.285 | 2.289 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.000 | 20 | 0.000 | 0.002 | random | 0.000 | 0.0 | 9.076 | 9.131 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.004 | 0.001 | 20 | 0.207 | 0.000 | k-means++ | 0.002 | 0.0 | 2.395 | 2.397 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.001 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 10.356 | 10.581 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000523 | 0.000525 | [20] | 1.529044 | 5.232027e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000899 | 0.001335 | 0.55 | 0.581698 | 1.041321 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.003353 | 0.000706 | [26] | 2.385863 | 3.353085e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.004047 | 0.000209 | 0.28 | 0.828616 | 0.829719 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 17.685 | 0.512 | [20] | 0.045 | 0.000 | 3.159 | 0.090 | 5.598 | 5.601 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 1.568 | 0.659 | [26] | 0.051 | 0.002 | 1.287 | 0.038 | 1.219 | 1.219 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.001 | 0.001 | [20] | 1.529 | 0.0 | 0.001 | 0.001 | 0.582 | 1.041 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.000 | [20] | 0.008 | 0.0 | 0.000 | 0.000 | 0.433 | 0.436 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.003 | 0.001 | [26] | 2.386 | 0.0 | 0.004 | 0.000 | 0.829 | 0.830 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.000 | [26] | 0.711 | 0.0 | 0.001 | 0.000 | 0.123 | 0.124 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.012924 | 0.000563 | NaN | 6.189802 | 0.000013 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.021388 | 0.001215 | 0.122191 | 0.604288 | 0.605262 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.327 | 0.007 | 0.244 | 0.0 | 0.337 | 0.004 | 0.971 | 0.971 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.467 | 0.100 | 0.545 | 0.0 | 0.479 | 0.216 | 3.060 | 3.356 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.013 | 0.001 | 6.190 | 0.0 | 0.021 | 0.001 | 0.604 | 0.605 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.000 | 0.903 | 0.0 | 0.000 | 0.000 | 0.576 | 0.583 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.000 | 4.661 | 0.0 | 0.000 | 0.000 | 0.523 | 0.672 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.000 | 0.009 | 0.0 | 0.000 | 0.000 | 0.476 | 0.618 | See | See |